Overexpression of CCNE1 confers a poorer prognostic in triple negative breast cancer identified by bioinformatic analysis

doi:10.21203/rs.3.rs-50005/v1

Download PDF

Research

Overexpression of CCNE1 confers a poorer prognostic in triple negative breast cancer identified by bioinformatic analysis

https://doi.org/10.21203/rs.3.rs-50005/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 23 Mar, 2021

Read the published version in World Journal of Surgical Oncology →

You are reading this older preprint version

Read the latest preprint version →

Background. Triple-negative breast cancer (TNBC) is a major subtype of breast cancer. Due to the lack of effective therapeutic targets, the prognosis is poor. In order to find an effective target, despite many efforts, the molecular mechanisms of TNBC are still not well understood which remain to be a profound clinical challenge.

Methods. To identify the candidate genes in the carcinogenesis and progression of TNBC, microarray datasets GSE36693 and GSE65216 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and functional and pathway enrichment analyses were performed using the Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) databases via DAVID. We constructed the protein-protein interaction network (PPI) and the performed the module analysis using STRING and Cytoscape. Then we reanalyzed the selected DEGs genes and the survival analysis was performed using cBioportal.

Results. A total of 140 DEGs were identified, consisting of 69 upregulated genes and 71 downregulated genes. Three hub genes were up-regulated among the selected genes from PPI and biological process analysis uncovered the fact that these genes were mainly enriched in p53 pathway and the pathways in cancer. Survival analysis showed that only CCNE1 may be involved in the carcinogenesis, invasion or recurrence of TNBC.

Conclusion. CCNE1 could confer a poorer prognostic in TNBC identified by bioinformatic analysis and play key roles in the progression of TNBC which may contribute potential targets for the diagnosis, treatment and prognosis assessment of TNBC.

Cancer Biology

Oncology

Triple negative breast cancer

GEO

CCNE1

Prognosis

Breast cancer is considered the most commonly diagnosed malignant tumor and the leading cause of cancer-related death among worldwide females [1]. Triple negative breast cancer (TNBC) is one of the main tumor subtypes of breast cancer, which lacks the expression of hormone receptors (estrogen/progesterone, ER/PR) and human epidermal growth factor receptor 2 (HER2) amplification, leading to a lack of effective treatment against the corresponding targets or receptors in addition to chemotherapy and radiotherapy. It accounts for 10–15% of all breast cancer [2], and it usually appears in the form of high-grade invasive ductal carcinoma and has a higher early recurrences rate of between the first and third year of treatment, with the majority of deaths occurring within the first 5 years [3]. And it often manifests as distant metastases and is associated with poorer prognosis with comparison to other breast cancer subtypes [4, 5].

However, due to not only the rapid development, the difficulty of early diagnosis and the high risk of metastasis of TNBC, but also the limited understanding of the pathogenesis, development, reproduction and molecular mechanisms of TNBC, the mortality rate remains high with only a few biomarkers are available for clinical use. Therefore, it is of great importance to illuminate the exact molecular mechanisms of carcinogenesis, proliferation and recurrence of TNBC in order to further develop more efficient diagnostic means as well as therapeutic options.

Microarray databases and bioinformatics analysis was entensively used in the past few decades for tumors to screen for genetic alterations at the genomic level. In recent years, there have been some bioinformatics studies on TNBC,which can help identify the DEG associated with TNBC carcinogenesis and progression and the functional pathways in which these genes participate, proving that integrated bioinformatical approaches is helpful for us to better exploring the potential mechanisms. Changes in expression levels can often reflect pathological conditions, and proteins encoded by these DEGs may be involved in different biological process, cellular behaviors, and molecular pathways during tumor progressions. In view of the diversity and complexity of TNBCs, omics technologies are necessary instruments to expand our understanding of TNBC subtypes [6]. As biomarkers, PR, ER and ERBB2/HER2 can be used as prognostic targets of breast carcinoma and are helpful to suggest the most appropriate chemotherapeutic treatments. BRCA1 and BRCA2 gene mutations and homologous recombination (HR) pathways etc. are involved in the DNA damage repair in TNBC [7]. All these highlighted the key role of genetic factors in the initiation and progression of TNBC. Cyclin D1 with its associated cyclin-dependent kinases, which are named CDK4 and CDK6, are core mediators in the G1 to S phase transition, CDK4/6 inhibitors are widely used in clinical practice based on this principle [8]. Transcriptomic analysis showed that many different genes such as MKI67, TOP2A, EGFR overexpressed in TNBC tumors. These omics data also allowed TNBC tumors to be differentiated, providing directions for treatment of different TNBC subtypes [6].

In the current study, two microarray datasets (GSE36693 and GSE65216) containing both TNBC tissue samples and non-cancer tissue samples were retrieved from Gene Expression Omnibus (GEO) and downloaded for further acquring DEGs .The DEGs in the two datasets above were obtained using the GEO2R online tool in their respective data sets and then using the Venn chart software to obtain the intersection. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze these DEGs including Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways, the former part containing biological process (BP), cellular component (CC), molecular function (MF). We constructed protein-protein interaction (PPI) network using STRING (Search Tool for the Retrieval of Interacting Genes), a convenient online software, and then applied Cytoscape as well as Molecular Complex Detection (MCODE) for extra analysis of DEGs so as to identify some hub-genes. Subsequently, re-analysis of important genes by GO and KEGG pathway enrichment were performed. Moreover, Gene Expression Profiling Interactive Analysis (GEPIA) was utilized to furtherly validated the DEGs expression between TNBC tissues and non-TNBC tissues. Then the survival of hub-genes was analyzed by cBioPortal.

2.1 Dataset selection

NCBI-GEO is considered as a public dataset containing countless microarray information [9], from which the gene expression profile of GSE36693 and GSE65216 in TNBC and non-TNBC tissues were downloaded. And the GSE36663 dataset contained 21 TNBC tissue samples and 66 non-TNBC samples. GSE65216 contained 55 TNBC samples and 109 non-TNBC samples.

2.2 Data processing and identification of DEGs

DEGs between TNBC samples and non-TNBC specimen were identified via GEO2R website (http://www.ncbi.nlm.nih.gov/geo/geo2r) with cut-off levels of log2 (fold-change) > 2 and adjust P value < 0.01 [10]. Then, through Venn software, the intersection of the unsettled data in TXT format was taken and graphed online to look for common DEGs in the two databases.

2.3 GO and KEGG enrichment analyses of DEGs

Gene ontology analysis (GO), considered as a widely-used approach to make a definition of genes and their downstream product to identify unique biological properties of high-throughput information [11]. KEGG is a collection of databases that manage genomes, diseases, biological pathways and relative chemical materials [12]. DAVID (http://david.ncifcrf.gov) is an online bioinformatic tool, utilized to integrate biological data and provides a complete series of functional annotation information of genes and their proteins for researchers to obtain biological data [13]. In this study, we used DAVID to perform enrichment analysis of DEG's Biological Process (BP), Molecular Function (MF), Cellular Component (CC) and its pathways (P < 0.05).

2.4 PPI network and module analysis

The online tool STRING (http://string-db.org) can be used to construct a PPI network [14]. Then, the results of STRING are further analyzed and visualized by Cytoscape to explore the possible association of these DEGs, with maximum number of interactors = 0 and confidence level ≥ 0.4, so as to further understand the pathogenesis or development mechanism of disease by analyzing protein functional interactions [15]. In addition, the Molecular Complex Detection (MCODE) of Cytoscape is an APP based on topological structure for clustering some known networks to find areas of their dense connectivity for further bioinformatic uses [16]. Thus, the MCODE was utilized to check modules of the PPI network (MCODE scores > 5, degree cutoff = 2, max. Depth = 100, k-core = 2, and node score cutoff = 0.2).

2.5 RNA sequencing expression of core genes

Gene Expression Profiling Interactive Analysis (GEPIA) is a network tool for cancer and normal gene expression profiling and interactive analyses to conduct differential expression analysis and survival analysis of genes of interest [17]. We utilized the GEPIA website to perform RNA expression analysis on these DEGs through TCGA data samples to verify.

2.6 Survival analysis

The cBioPortal online platform provides a visual analysis instrument for interactive exploration of diverse cancer genome datasets [18, 19], and Users can perform survival analysis based on DNA mutation data and CNA data, and visually display the patient's OS and DFS results in the form of Kaplan-Meier diagrams [20]. Draw Kaplan-Meier curve through cBioPortal to analyze the overall survival of hub genes. We then calculate the hazard ratio (HR) and log rank P value with 95% confidence interval, and display it in the figure.

3.1 Identification of DEGs in TNBC

The flow diagram of our study is summarized in Fig. 1. There were 76 TNBC tissues and 175 non-TNBC tissues in the current research. 147 and 391 DEGs from GSE36693 and GSE65216 were respectively extracted by GEO2R online tools. 157 up-regulated and 130 down-regulated DEGs were conducted in GSE36693 (Fig. 2A) with the criteria of |logFC| > 2 and adjusted p-value < 0.05; 282 up-regulated and 249 down-regulated DEGs were gained in GSE65216 (Fig. 2B). Then, Venn diagram online tool was utilized to intersect the DEGs in the two datasets and visualize them to identify the common DEGs. Consequences were that totally 140 commonly DEGs were detected in the TNBC tissues, including 69 down-regulated genes with the limitation of logFC<-2, and 71 up-regulated genes with the limitation of logFC > 2 (Fig. 2C).

3.2 DEGs GO and KEGG pathway analysis in TNBC

All 140 DEGs were included by DAVID online tool and the results of GO analysis indicated that: 1) When it comes to biological processes (BP), those up-regulated genes were mainly enriched in peripheral nervous system development, epidermis development, single organismal cell-cell adhesion, positive regulation of transcription from RNA polymerase II promoter, cytoskeleton organization etc., while down-regulated DEGs were enriched in phosphatidylinositol 3-kinase signaling, positive regulation of transcription from RNA polymerase II promoter, regulation of intracellular transport, wound healing and so on; 2) When metioning molecular function (MF), up-regulated DEGs were enriched in transcription factor activity, RNA polymerase II distal enhancer sequence-specific binding, chitinase activity, chitin binding, and down-regulated DEGs were significantly enriched in RNA polymerase II transcription factor binding, calcium ion binding, estrogen response element binding, dystroglycan binding, transcription regulatory region DNA binding; 3) For cell component (CC), up-regulated DEGs were notably enriched in the extracellular space, extracellular exosome, epidermal lamellar body, intermediate filament while down-regulated genes were mainly enriched in extracellular space (Table 1).

Table 1

Gene Ontology analysis of differentially expressed genes in TNBC.
Expression	Category	Term	Count	%	P Value
Up-regulated	GOTERM_BP_DIRECT	GO:0007422 ~ peripheral nervous system development	3	4.35	0.0036254
	GOTERM_BP_DIRECT	GO:0008544 ~ epidermis development	4	5.80	0.003993946
	GOTERM_BP_DIRECT	GO:0016337 ~ single organismal cell-cell adhesion	4	5.80	0.006458971
	GOTERM_CC_DIRECT	GO:0005615 ~ extracellular space	16	23.19	7.34E-05
	GOTERM_CC_DIRECT	GO:0005882 ~ intermediate filament	4	5.80	0.008000418
	GOTERM_CC_DIRECT	GO:0070062 ~ extracellular exosome	19	27.54	0.009474606
	GOTERM_CC_DIRECT	GO:0097209 ~ epidermal lamellar body	2	2.90	0.014409065
	GOTERM_MF_DIRECT	GO:0004568 ~ chitinase activity	2	2.90	0.024620645
	GOTERM_MF_DIRECT	GO:0008061 ~ chitin binding	2	2.90	0.028088866
Down -regulated	GOTERM_BP_DIRECT	GO:0014065 ~ phosphatidylinositol 3-kinase signaling	3	0.03	0.004160756
	GOTERM_BP_DIRECT	GO:0045944 ~ positive regulation of transcription from RNA polymerase II promoter	10	0.10	0.00769136
	GOTERM_CC_DIRECT	GO:0005615 ~ extracellular space	12	0.12	0.007648324
	GOTERM_MF_DIRECT	GO:0001085 ~ RNA polymerase II transcription factor binding	3	0.03	0.012119346
	GOTERM_MF_DIRECT	GO:0005509 ~ calcium ion binding	8	0.08	0.013256787
	GOTERM_MF_DIRECT	GO:0034056 ~ estrogen response element binding	2	0.02	0.014142799

KEGG analysis results were shown in Table 2 and Fig. 3B, they indicated that DEGs were significantly enriched in p53 signaling pathway, Prostate cancer and Metabolic pathways (P < 0.05).

Table 2

KEGG pathway analysis of differentially expressed genes in TNBC.
Category	Term	Count	%	P Value
KEGG_PATHWAY	hsa04115: p53 signaling pathway	4	2.88	0.008797166
KEGG_PATHWAY	hsa05215: Prostate cancer	4	2.88	0.018367061
KEGG_PATHWAY	hsa01100: Metabolic pathways	14	10.07	0.037159241

3.3 Protein–protein interaction network (PPI) analysis

Altogether 140 DEGs including 69 up-regulated genes and 71 down-regulated genes were imported into the DEGs PPI network complex which contains 94 nodes and 180 edges (Fig. 4A). There were total 46 isolated nodes which were not included in it. Next, Cytoscape MCODE was applied and results showed that 29 central nodes were identified among the 94 nodes (Fig. 4B).

3.4 Re-analysis of 29 selected genes by GO and KEGG pathway enrichment

For the sake of figuring out the possible pathways of the 29 selected DEGs and verify whether they are consistent with the result of 140 DEGs enrichment analysis, the GO and KEGG pathway enrichment analysis was performed on these 29 DEGs again by DAVID (P < 0.05). Consequences reported that these DEGs evidently enriched in the p53 signaling pathway, pathways in cancer, oocyte meiosis and prostate cancer (P < 0.05, Tables 3 and 4 & Fig. 5).

Table 3

Re-analysis of 29 selected genes via Gene Ontology enrichment.
Category	Term	Count	P Value
GOTERM_BP_DIRECT	GO:0045893 ~ positive regulation of transcription, DNA-templated	9	9.84E-07
GOTERM_BP_DIRECT	GO:0045944 ~ positive regulation of transcription from RNA polymerase II promoter	11	1.50E-06
GOTERM_BP_DIRECT	GO:0006366 ~ transcription from RNA polymerase II promoter	7	1.35E-04
GOTERM_BP_DIRECT	GO:0048546 ~ digestive tract morphogenesis	3	1.92E-04
GOTERM_BP_DIRECT	GO:0043568 ~ positive regulation of insulin-like growth factor receptor signaling pathway	3	1.92E-04
GOTERM_BP_DIRECT	GO:0014065 ~ phosphatidylinositol 3-kinase signaling	3	8.52E-04
GOTERM_CC_DIRECT	GO:0005882 ~ intermediate filament	4	6.79E-04
GOTERM_CC_DIRECT	GO:0005615 ~ extracellular space	9	7.11E-04
GOTERM_CC_DIRECT	GO:0005634 ~ nucleus	16	0.006994347
GOTERM_CC_DIRECT	GO:0070062 ~ extracellular exosome	10	0.020917677
GOTERM_CC_DIRECT	GO:0000790 ~ nuclear chromatin	3	0.035216711
GOTERM_CC_DIRECT	GO:0030057 ~ desmosome	2	0.036252678
GOTERM_MF_DIRECT	GO: 0008134 ~ transcription factor binding	7	5.93E-06
GOTERM_MF_DIRECT	GO: 0043565 ~ sequence-specific DNA binding	7	1.72E-04
GOTERM_MF_DIRECT	GO: 0000981 ~ RNA polymerase II transcription factor activity, sequence-specific DNA binding	5	1.72E-04
GOTERM_MF_DIRECT	GO: 0044212 ~ transcription regulatory region DNA binding	5	3.98E-04
GOTERM_MF_DIRECT	GO: 0005515 ~ protein binding	24	9.40E-04
GOTERM_MF_DIRECT	GO: 0001085 ~ RNA polymerase II transcription factor binding	3	0.002738639

Table 4

Re-analysis of 29 selected genes via KEGG pathway enrichment.
Category	Term	Count	P Value	Genes
KEGG_PATHWAY	hsa04115: p53 signaling pathway	4	1.38E-04	CCNE1, CDKN2A, SERPINB5, IGF1
KEGG_PATHWAY	hsa05215: Prostate cancer	3	0.008256173	CCNE1, AR, IGF1
KEGG_PATHWAY	hsa04114: Oocyte meiosis	3	0.012905535	CCNE1, AR, IGF1
KEGG_PATHWAY	hsa05200: Pathways in cancer	4	0.021651652	CCNE1, AR, CDKN2A, IGF1

3.5 Analysis of core genes by the GEPIA

After taking the results of both PPI analysis and the KEGG pathway enrichment into consideration, we found that CCNE1, CDKN2A, AR, SERPINB5 and IGF1 among the 29 selected genes could play a key role in common significantly enriched pathways. Then, GEPIA was utilized to analyze the differences in the expression of these 5 genes of TNBC and non-TNBC tissues. It was found that compared with non-TBNC samples, three genes were highly expressed in TNBC samples (P < 0.05, Table 5 & Fig. 6).

Table 5

Validation of five genes via GEPIA.
Category	Genes
Genes with high expressed in TNBC (P < 0.05)	CCNE1, AR, CDKN2A
Genes without high expressed in TNBC (P > 0.05)	SERPINB5, IGF1

3.6 Survival analysis using cBioportal

cBioportal was used to identify 3 hub genes survival data. The results showed that only CCNE1 had a obviously worse survival, while the other two did not (P < 0.05, and Fig. 7).

In order to recognize meaningful prognostic biomarkers of TNBC, this research analyzed two profile data sets (GSE36693 and GSE 65216) using bioinformatics methods. A total of 76 TNBC specimens and 175 non-TNBC specimens were included in the current research. Through the online tool analysis of GEO2R and Venn diagram, we found 140 common DEGs in total (|logFC|>2 and adjust P < 0.01). Then, the results of GO and KEGG Pathway Enrichment Analysis utilizing DAVID online tool were as followed: 1) when mentioning to biological processes (BP), up-regulated DEGs were significantly enriched in peripheral nervous system development, epidermis development, single organismal cell-cell adhesion, positive regulation of transcription from RNA polymerase II promoter, cytoskeleton organization etc., while down-regulated DEGs were enriched in phosphatidylinositol three-kinase signaling, positive regulation of transcription from RNA polymerase II promoter, regulation of intracellular transport, wound healing and so on; 2) when it comes to molecular function (MF), up-regulated DEGs were particularly enriched in transcription factor activity, RNA polymerase II distal enhancer sequence-specific binding, chitinase activity, chitin binding, and down-regulated DEGs were significantly enriched in RNA polymerase II transcription factor binding, calcium ion binding, estrogen response element binding, dystroglycan binding, transcription regulatory region DNA binding; 3) for cell component (CC), up-regulated DEGs were enriched in the extracellular space, extracellular exosome, epidermal lamellar body, intermediate filament while down-regulated DEGs were significantly enriched in extracellular space. For KEGG pathway analysis, DEGs were obviously enriched in p53 signaling pathway, prostate cancer and metabolic pathways (P < 0.05). Then, by using STRING online tool as well as Cytoscape, we constructed DEGs PPI network complex composed of 94 nodes and 180 edges. In addition, 29 vital genes were screened from the PPI network complex via the analysis of Cytoscape's MCODE. Next, we performed KEGG enrichment analysis again on 29 DEGs through DAVID and found that these genes were enriched in the p53 signaling pathway, pathways in cancer, oocyte meiosis and prostate cancer (P < 0.05). After taking the results of both PPI analysis and the KEGG pathway enrichment into consideration, we found that CCNE1, CDKN2A, AR, SERPINB5 and IGF1 among the 29 selected genes could play a key role in common significantly enriched pathways. The 5 genes were verified by GEPIA analysis, and it was found that compared with non-TNBC samples, 3 genes showed high expression in TNBC samples (P < 0.05). Finally, through cBioportal, we found that only CCNE1 had a significantly worse survival, which could be used as a new potential target for providing new treatment ideas for TNBC and improving patient prognosis..

CCNE1, also known as cyclin E1, is encoded by this gene belongs to the highly conserved cyclin family. The characteristic of its members is that the protein concentration changes drastically with the cycle throughout the cell cycle. Cyclin is a regulator of cyclin-dependent kinase (CDK), CCNE1 forms a complex with CDK2 as a regulatory subunit whose activity is necessary for cell cycle G1/S transition [21]. The protein is abundantly present at the boundary of G1-S phase and degraded as the cell cycle passes through S phase. This gene has been observed to be highly expressed in many tumors, which can lead to chromosomal instability and contribute to tumorigenesis. The dysregulation of CCNE1-CDK2 activity is related to a variety of cancers including nasopharyngeal carcinoma, bladder cancer and breast cancer, and has been fully proven [22–24]. Accumulating data proved that TNBC frequently expressed CCNE1, while ER positive cancer did not [25], and the absence of CCNE1 for poorer DFS [25]. In the p53 pathway, p53 acted as a tumor suppressor gene. But Contrary to the activation of p53 regulatory checkpoint or apoptosis, the expression of cyclin E protein promotes the process of entering S phase from G1 phase. And the lack of p53 function gives tumor cells an escape gap, so that tumor cells can avoid cell cycle arrest or cell death, and advance to the next stage through this disorder and uncontrolled growth [21, 26]. In addition, the loss of functional expression of the G1 checkpoint CDK inhibitor, p21 (CDKN1A) is also related to the carcinogenesis and disease progression of breast cancer, at the same time, more and more data indicate that the loss of function of p21 can mediate the drug-resistant phenotype which always means a poor prognosis [27]. In our bioinformatics analysis, the phenomenon of the up-regulated CCNE1 enriched in the p53 pathway was verified again and it CCNE1 really plays an important role in TNBC, but relative clinical practice is lacking.

As for pathways in cancer, specifically in the cell cycle, the transcription factor E2F1 and the tumor suppressor protein retinoblastoma (RB) are two key factors that regulate the progression of the cell cycle. They determine whether the cell can carry out the process of DNA replication and cell division by regulating the checkpoints of G1/S and G2/M together [28]. The CCNE1/CDK2 complex can phosphorylate RB, then release E2F1 and activate its transcriptional activity to advance the cell cycle from G1 to S phase, while dephosphorylation of RB promotes E2F1 heterodimerization while inhibiting E2F1 activity [29, 30]. It can be seen in the Fig. 7B that Cyclin E-CDK2 is associated with RB gene in the process of pathways in cancer, and this enrichment of CCNE1 was validated by us as well. There is no doubt that CCNE1 can be a useful target of TNBC in the future.

Numerous studies have proved that CCNE1 was related to the carcinogenesis and development of various types of cancer. Nevertheless, researches and clinical practice reported about this gene in TNBC is insufficient, in other words, they haven’t been taken seriously enough. Further experiments should be carried out on CCNE1 to better validate its functions. Thus, results in our research may provide useful information and directions for prospective research on TNBC treatment and prognosis. But this needs to be further verified through experiments in vitro or in vivo.

Taken above, CCNE1 could lead to a poorer prognostic in TNBC identified via bioinformatic analysis and play key roles in the progression of TNBC which may contribute potential targets for the diagnosis, treatment and prognosis assessment of TNBC.

CONFLICT OF INTERESTS

The authors report no conflict of interest in this work.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Not applicable.

DATA AVAILABILITY STATEMENT

All the data in this research are available from GEO database and TCGA database.

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018. https://doi.org/10.3322/caac.21492
Siegel RL, Miller KD, Jemal A: Cancer statistics, 2019. CA Cancer J Clin 2019. https://doi.org/10.3322/caac.21551
Rakha EA, Reis-Filho JS, Ellis IO: Basal-like breast cancer: a critical review. J Clin Oncol 2008. https://doi.org/10.1200/JCO.2007.13.1748
Vagia E, Mahalingam D, Cristofanilli M: The Landscape of Targeted Therapies in TNBC. Cancers (Basel) 2020. https://doi.org/10.3390/cancers12040916
Malorni L, Shetty PB, De Angelis C, Hilsenbeck S, Rimawi MF, Elledge R,... Arpino G: Clinical and biologic features of triple-negative breast cancers in a large cohort of patients with long-term follow-up. Breast Cancer Res Treat 2012. https://doi.org/10.1007/s10549-012-2315-y
Judes G, Rifaï K, Daures M, Dubois L, Bignon YJ, Penault-Llorca F,... Bernard-Gallon D: High-throughput «Omics» technologies: New tools for the study of triple-negative breast cancer. Cancer Lett 2016. https://doi.org/10.1016/j.canlet.2016.03.001
Evans DG, Howell A, Ward D, Lalloo F, Jones JL, Eccles DM: Prevalence of BRCA1 and BRCA2 mutations in triple negative breast cancer. J Med Genet 2011. https://doi.org/10.1136/jmedgenet-2011-100006
Roy PG, Thompson AM: Cyclin D1 and breast cancer. Breast 2006. https://doi.org/10.1016/j.breast.2006.02.005
Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002. https://doi.org/10.1093/nar/30.1.207
Davis S, Meltzer PS: GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007. https://doi.org/10.1093/bioinformatics/btm254
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM,... Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000. https://doi.org/10.1038/75556
Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000. https://doi.org/10.1093/nar/28.1.27
Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009. https://doi.org/10.1038/nprot.2008.211
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J,... von Mering C: STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 2015. https://doi.org/10.1093/nar/gku1003
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D,... Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003. https://doi.org/10.1101/gr.1239303
Bandettini WP, Kellman P, Mancini C, Booker OJ, Vasu S, Leung SW,... Arai AE: MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson 2012. https://doi.org/10.1186/1532-429X-14-83
Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z: GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017. https://doi.org/10.1093/nar/gkx247
Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO,... Schultz N: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013. https://doi.org/10.1126/scisignal.2004088
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA,... Schultz N: The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012. https://doi.org/10.1158/2159-8290.CD-12-0095
Zheng H, Zhang G, Zhang L, Wang Q, Li H, Han Y,... Guo X: Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis. Front Oncol 2020. https://doi.org/10.3389/fonc.2020.00068
Lopez-Beltran A, MacLennan GT, Montironi R: Cyclin E as molecular marker in the management of breast cancer: a review. Anal Quant Cytol Histol 2006.
Chen M, Wu R, Li G, Liu C, Tan L, Xiao K,... Qin Z: Motor neuron and pancreas homeobox 1/HLXB9 promotes sustained proliferation in bladder cancer by upregulating CCNE1/2. J Exp Clin Cancer Res 2018. https://doi.org/10.1186/s13046-018-0829-9
Wu J, Lv Q, He J, Zhang H, Mei X, Cui K,... Zhang Y: MicroRNA-188 suppresses G1/S transition by targeting multiple cyclin/CDK complexes. Cell Commun Signal 2014. https://doi.org/10.1186/s12964-014-0066-6
Luo Q, Li X, Li J, Kong X, Zhang J, Chen L,... Fang L: MiR-15a is underexpressed and inhibits the cell cycle by targeting CCNE1 in breast cancer. Int J Oncol 2013. https://doi.org/10.3892/ijo.2013.2034
Zagouri F, Kotoula V, Kouvatseas G, Sotiropoulou M, Koletsa T, Gavressea T,... Fountzilas G: Protein expression patterns of cell cycle regulators in operable breast cancer. Plos One 2017. https://doi.org/10.1371/journal.pone.0180489
Barton MC, Akli S, Keyomarsi K: Deregulation of cyclin E meets dysfunction in p53: closing the escape hatch on breast cancer. J Cell Physiol 2006. https://doi.org/10.1002/jcp.20818
Abukhdeir AM, Park BH: P21 and p27: roles in carcinogenesis and drug resistance. Expert Rev Mol Med 2008. https://doi.org/10.1017/S1462399408000744
Kanska J, Zakhour M, Taylor-Harding B, Karlan BY, Wiedemeyer WR: Cyclin E as a potential therapeutic target in high grade serous ovarian cancer. Gynecol Oncol 2016. https://doi.org/10.1016/j.ygyno.2016.07.111
Sheldon LA: Inhibition of E2F1 activity and cell cycle progression by arsenic via retinoblastoma protein. Cell Cycle 2017. https://doi.org/10.1080/15384101.2017.1338221
Liu F, Cai Y, Rong X, Chen J, Zheng D, Chen L,... Ruan J: MiR-661 promotes tumor invasion and metastasis by directly inhibiting RB1 in non-small cell lung cancer. Mol Cancer 2017. https://doi.org/10.1186/s12943-017-0698-4

Download PDF

Journal Publication

published 23 Mar, 2021

Read the published version in World Journal of Surgical Oncology →

Review #2 received at journal
20 Dec, 2020
Review #3 received at journal
19 Dec, 2020
Reviewer #5 agreed at journal
14 Dec, 2020
Reviewer #4 agreed at journal
14 Dec, 2020
Reviewer #3 agreed at journal
13 Dec, 2020
Review #1 received at journal
14 Sep, 2020
Reviewer #2 agreed at journal
31 Aug, 2020
Reviewer #1 agreed at journal
20 Aug, 2020
Reviewers invited by journal
30 Jul, 2020
Editor assigned by journal
29 Jul, 2020
First submitted to journal
28 Jul, 2020
Submission checks completed at journal
28 Jul, 2020
Editor invited by journal
28 Jul, 2020

You are reading this older preprint version

Read the latest preprint version →

Overexpression of CCNE1 confers a poorer prognostic in triple negative breast cancer identified by bioinformatic analysis

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Dataset selection

2.2 Data processing and identification of DEGs

2.3 GO and KEGG enrichment analyses of DEGs

2.4 PPI network and module analysis

2.5 RNA sequencing expression of core genes

2.6 Survival analysis

3. Results

3.1 Identification of DEGs in TNBC

3.2 DEGs GO and KEGG pathway analysis in TNBC

3.3 Protein–protein interaction network (PPI) analysis

3.4 Re-analysis of 29 selected genes by GO and KEGG pathway enrichment

3.5 Analysis of core genes by the GEPIA

3.6 Survival analysis using cBioportal

4. Discussion

5. Conclusions

Declarations

References

Status:

Journal Publication

Version 1